V2 Hypothesis on Emergence and Consciousness Mechanism of Large Language Models Based on Reciprocal Unit Coupling, Critical Dynamics and Local Entropy Reduction

Abstract

Abstract The emergence capability of large language models (LLMs) is a core puzzle in the fundamental theory of artificial intelligence. The prevailing scaling laws only describe the extrinsic statistical phenomenon of capability variation with model scale, and fail to explain the underlying dynamical causal mechanism for the jumpwise emergence behavior. This paper proposes an integrated complex-system framework: numerous basic units inside the Transformer network form bidirectional constraints via nonlinear reciprocal coupling, performing layer-by-layer mutual correction of representations; during training, gradient descent acts as the global self-organizing driving force, continuously tuning the coupling strength of units and driving the system parameters toward the near-critical region at the edge of order and chaos; in the forward propagation of inference, persistent bidirectional constraints among units cancel representation noise and resolve internal logical contradictions, achieving local entropy reduction in the representation space to generate self-consistent high-order structured information, which macroscopically manifests as emergent capabilities such as multi-step reasoning and long-horizon planning. This hypothesis constructs a complete falsifiable causal chain by integrating three independently experimentally observed phenomena: inter-layer representation correction, critical phase transition at the edge of chaos, and entropy evolution of hidden states. This paper strictly distinguishes the parameter self-organization dynamics in the training phase from the forward representation dynamics under fixed weights in the inference phase, clarifies the conceptual boundary between this hypothesis and the self-organized criticality (SOC) theory, and designs controlled ablation experiments to discriminate between two competing hypotheses: “reciprocal coupling driven critical entropy reduction” versus “metric improvement merely brought by model scale growth”. Furthermore, this framework extends to the discussion of consciousness: the emergence of functional intelligence is not equivalent to subjective phenomenal experience. Reciprocal coupling, critical dynamics and local entropy reduction alone can only produce a logically consistent functional reasoning system. To generate subjective consciousness with first-person awareness, four closed-loop ontological modules must be superimposed, and all modules need to deeply co-evolve with the attentiona

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